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--- |
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base_model: |
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- Qwen/Qwen2.5-14B-Instruct |
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datasets: |
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- LLM4Code/expanded_origen_126k |
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license: apache-2.0 |
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tags: |
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- Verilog |
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- CodeGen |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# VeriCoder: Enhancing LLM-Based RTL Code Generation through Functional Correctness Validation |
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This repository hosts **VeriCoder**, a model presented in the paper [VeriCoder: Enhancing LLM-Based RTL Code Generation through Functional Correctness Validation](https://huggingface.co/papers/2504.15659). |
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VeriCoder is a model for Register Transfer Level (RTL) code generation fine-tuned on a dataset validated for functional correctness. This fine-tuning dataset is constructed using a novel methodology that combines unit test generation with feedback-directed refinement. Given a natural language specification and an initial RTL design, a teacher model iteratively revises the RTL design based on simulation results using generated tests. Every example in the dataset is functionally validated, consisting of a natural language description, an RTL implementation, and passing tests. |
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For more details and code, visit the [GitHub Repository](https://github.com/Anjiang-Wei/VeriCoder). |
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## Key Highlights |
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- **Functionally Validated Dataset**: 125,000+ examples with simulation-passing RTL designs. |
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- **Feedback-Driven Construction**: Iteratively refine designs and tests based on test results. |
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- **Superior Performance**: Achieves up to +71.7% relative improvement on VerilogEval benchmarks. |
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- **Comprehensive Resources**: Includes dataset, model weights, inference scripts, and training pipeline. |
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## Citation |
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If you find VeriCoder helpful in your research, please consider citing: |
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```plaintext |
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@article{wei2025vericoder, |
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title={VeriCoder: Enhancing LLM-Based RTL Code Generation through Functional Correctness Validation}, |
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author={Wei, Anjiang and Tan, Huanmi and Suresh, Tarun and Mendoza, Daniel and Teixeira, Thiago SFX and Wang, Ke and Trippel, Caroline and Aiken, Alex}, |
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journal={arXiv preprint arXiv:2504.15659}, |
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year={2025} |
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} |
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``` |
